From A100 to H200: How to Choose the Right GPU for Training & Inference in 2025
TLDR
- A100 → H100 → H200 marks a major performance leap.
- Your choice should depend on memory needs, compute demands, and cost per workload.
- A100s remain highly cost-efficient for training and fine-tuning.
- H100s deliver excellent throughput for inference.
- H200’s 141GB VRAM unlocks memory-heavy and long-context models.
- Aquanode’s multi-cloud GPU marketplace makes switching between these GPUs easy and cost-effective.
The GPU landscape has changed more in two years
The GPU landscape has evolved rapidly, and 2025 brings the biggest gap in capability since the V100 era. As teams train and deploy larger models, the real question becomes which GPU offers the best cost-performance for their workflow.
Matching GPU specs to your workload matters, but so does flexibility. Aquanode helps developers compare and deploy A100, H100 and H200 instances from multiple providers through one account.
A100 vs H100 vs H200: What actually matters
1. Memory Capacity
- A100: 40GB or 80GB
- H100: 80GB
- H200: 141GB
Memory has become the limiting factor for many LLM and multimodal workloads. Models that push beyond 80GB benefit significantly from the H200. On Aquanode, teams choose H200s for long-context LLMs, high-concurrency inference, and larger batch sizes without micro-batching.
2. Raw Compute and Architecture
Hopper GPUs (H100 and H200) bring transformer-optimized kernels, FP8 acceleration and higher throughput. This often results in two to four times faster training and even larger gains for inference. Many teams on Aquanode upgrade from A100s to H100s when production workloads demand more throughput.
3. Cost-Performance
Hourly pricing is misleading; the real metric is cost per completed run. An H100 that finishes a job in a third of the time can be cheaper than an A100. An H200 that avoids sharding or reduces parallelism overhead can shorten epochs significantly.
Aquanode’s marketplace makes this easy to evaluate by showing side-by-side pricing across multiple cloud providers and enabling quick switching when prices shift.
So which GPU is best for your workload in 2025?
If you’re fine-tuning models on a budget
Pick: A100
- Fit in 40GB or 80GB
- Don’t require Hopper features
- Benefit more from cheaper hourly pricing
A100s remain the price-efficiency leader for small and mid-sized teams.
If you’re training medium or large transformer models
Pick: A100 or H100
- If cost sensitivity matters → A100
- If you want high throughput → H100
Unless your model exceeds 80GB or needs big batches, the A100 still offers unbeatable value.
If you’re training or serving LLMs with long context
Pick: H200
- 141GB VRAM
- 128k+ token context
- Large mixture-of-experts
- Multimodal LLMs
- Inference servers running many requests concurrently
If your model strains 80GB or doesn’t fit at all, H200 is the natural upgrade.
If you’re running high-volume inference
Pick: H100 or H200
- Big batches
- High throughput
- FP8 acceleration
- Transformer-engine optimizations
In 2025, Hopper-based GPUs outperform A100s dramatically for inference workloads.
The underrated factor: Flexibility across providers
GPU pricing, availability and regions vary widely across cloud platforms. Relying on a single provider can slow development or inflate costs.
Aquanode solves this by offering:
- One account for multiple cloud providers
- A unified dashboard for A100, H100 and H200
- Pause and resume features
- Easy provider switching
- Consistent pricing visibility across regions
In modern AI development, flexibility is as important as raw performance.
How to choose your GPU in under 60 seconds
Ask yourself:
-
Does your model fit in 80GB?
- Yes → A100 or H100
- No → H200
-
Is cost your priority?
- A100
-
Is speed your priority?
- H100
-
Is your workload memory-bound?
- H200
-
Do you want to avoid cloud lock-in?
- Use Aquanode to switch providers easily
Final Thoughts
GPU choice now has a dramatic impact on training and inference velocity. The A100 remains a dependable workhorse, the H100 delivers unmatched throughput, and the H200 opens the door to long-context and memory-intensive models.
Aquanode enables teams to choose the right GPU for each stage of their workflow without being tied to a single cloud’s pricing or availability.
